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arXiv · 2011.05989

Linear Dilation-Erosion Perceptron for Binary Classification

Angelica Lourenço Oliveira·Marcos Eduardo Valle
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Abstract

In this work, we briefly revise the reduced dilation-erosion perceptron (r-DEP) models for binary classification tasks. Then, we present the so-called linear dilation-erosion perceptron (l-DEP), in which a linear transformation is applied before the application of the morphological operators. Furthermore, we propose to train the l-DEP classifier by minimizing a regularized hinge-loss function subject to concave-convex restrictions. A simple example is given for illustrative purposes.

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Angelica Lourenço Oliveira, Marcos Eduardo Valle. 2020-11-11. Linear Dilation-Erosion Perceptron for Binary Classification. https://arxiv.org/abs/2011.05989

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